The present technology relates to a learning device, a learning method, and a program, and more particularly, to a learning device, a learning method, and a program capable of obtaining a highly accurate classifier at a higher speed.
Although a large number of learning images for hand shapes are necessary, for example, in learning of a multi-class object recognizer such as a hand-shape detector, the learning is time-consuming if a large number of learning images are used. Transfer learning capable of reducing a learning time using previously obtained knowledge has been proposed (for example, see L. Torrey and J. Shavlik, “Transfer Learning,” In E. Soria, J. Martin, R. Magdalena, M. Martinez and A. Serrano, editors, Handbook of Research on Machine Learning Applications, IGI Global 2009; and Sinno Jialin Pan and Qiang Yang, “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 10, pp 1345 to 1359, October 2010).
In addition, recently, object recognition systems using transfer learning have been proposed (for example, see L. Fei-Fei, R. Fergus and P. Perona, “One-Shot learning of object categories,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp 594 to 611, 2006; E. Bart, S. Ullman, “Cross-generalization: learning novel classes from a single example by feature replacement,” in Proc. CVPR, 2005; and M. Stark, M. Goesele and B. Schiele, “A Shape-Based Object Class Model for Knowledge Transfer,” Twelfth IEEE International Conference on Computer Vision (ICCV), 2009, Kyoto, Japan (2009)).
In these object recognition systems, objects are expressed by small parts and appearance and location distributions of the parts are learned and unknown classes are learned by transferring distributions of known classes. In addition, the object recognition systems use a framework of Bayesian estimation and focus on learning of one or more samples or a small number of samples.